Eigenskies: a method of visualizing weather prediction data

Bjorn Olsson; Anders Ynnerman; Reiner Lenz

doi:10.1117/12.473931

9 June 2003 Eigenskies: a method of visualizing weather prediction data

Bjorn Olsson, Anders Ynnerman, Reiner Lenz

Proceedings Volume 5009, Visualization and Data Analysis 2003; (2003) https://doi.org/10.1117/12.473931
Event: Electronic Imaging 2003, 2003, Santa Clara, CA, United States

Abstract

Visualizing a weather prediction data set by actually synthesizing an image of the sky is a difficult problem. In this paper we present a method for synthesizing realistic sky images from weather prediction and climate prediction data. Images of the sky are combined with a number of weather parameters (like pressure and temperature) to train an artificial neural network (ANN) to predict the appearance of the sky from certain weather parameters. Hourly measurements from a period of eight months are used. The principal component analysis (PCA) method is used to decompose images of the sky into their eigen components -- the eigenskies. In this way the image information is compressed into a small number of coefficients while still preserving the main information in the image. This means that the fine details of the cloud cover cannot be synthesized using this method. The PCA coefficients together with measured weather parameters at the same time form a data point that is used to train the ANN. The results show that the method gives adequate results and although some discrepancies exist, the main appearance is correct. It is possible to distinguish between different types of weather. A rainy day looks rainy and a sunny day looks sunny.

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Bjorn Olsson, Anders Ynnerman, and Reiner Lenz "Eigenskies: a method of visualizing weather prediction data", Proc. SPIE 5009, Visualization and Data Analysis 2003, (9 June 2003); https://doi.org/10.1117/12.473931

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KEYWORDS

Principal component analysis

Visualization

Neural networks

Clouds

Neurons

Artificial neural networks

Image compression

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